William David Travis

This study examines ecDNA in lung cancer, finding it in 18.9% of patients and associating it with worse survival and genomic instability.

This paper develops a deep learning model to predict molecular alterations in never-smoker lung adenocarcinomas from histological images, achieving high accuracy for most features.

The paper identifies three subtypes of NS-LUAD using RNA-seq data and develops a 60-gene signature that predicts survival across subgroups.

This study analyzes 1024 lung adenocarcinomas to identify evolutionary patterns and mutational mechanisms, revealing distinct trajectories linked to smoking, ancestry, and retrotransposition.